Cloud application error analysis method and device, equipment, medium and program product

Abstract

The invention discloses a cloud application error analysis method and device, equipment, a medium and a program product, and relates to the technical field of cloud application, and the cloud application error analysis method comprises the steps: obtaining a historical error log of at least one cloud application, and extracting an error code from each error log; obtaining a historical error analysis result of each error code; performing model training on a preset error code analysis model by taking each error code and the corresponding historical error analysis result as training data to obtain a trained target error code analysis model; and detecting an error log of at least one cloud application in real time, if it is detected that an unanalyzed error code exists in the error log, analyzing the error log corresponding to the unanalyzed error code based on the target error code analysis model, and determining a model analysis result. According to the invention, the error processing efficiency and accuracy can be improved.

Description

Technical Field

[0001] The present application relates to the field of cloud application technology, and in particular to a cloud application error analysis method, device, equipment, medium and program product. Background Art

[0002] With the rapid development of financial technology, financial cloud services have become one of the core infrastructures supporting the business operations of various financial institutions. However, with the increase in application complexity and the surge in business volume, higher requirements are placed on the monitoring and maintenance of applications on cloud platforms. Although the application monitoring technology on the current market has made certain progress, it still has some significant limitations and defects. Although some existing monitoring tools can capture error information in real time, they still rely too much on manual review of logs and stack trace information in the analysis and location of the cause of the error. This reliance is not only time-consuming and labor-intensive, reducing the efficiency of problem solving, but also when processing large amounts of log data, manual analysis is prone to omissions, leading to misjudgments, affecting the speed and quality of system recovery.

[0003] Therefore, manual error analysis of cloud applications can easily lead to low error handling efficiency and accuracy, which has become a problem that needs to be solved urgently.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a cloud application error analysis method, device, equipment, medium and program product, aiming to solve the technical problem that manual error analysis of cloud applications easily leads to low error handling efficiency and accuracy.

[0006] To achieve the above objectives, the present application proposes a cloud application error analysis method, which includes: Obtaining historical error logs of at least one cloud application, and extracting error codes from each of the error logs; Obtaining historical error analysis results for each of the error codes; Using each of the error codes and the corresponding historical error analysis results as training data to perform model training on a preset error code analysis model, to obtain a trained target error code analysis model, wherein the error code analysis model is used to predict the error analysis results of unanalyzed error codes, where the unanalyzed error codes are error codes for which there are no historical error analysis results; The error log of at least one cloud application is detected in real time. If an unanalyzed error code is detected in the error log, the error log corresponding to the unanalyzed error code is analyzed based on the target error code analysis model to determine a model analysis result.

[0007] In one embodiment, the step of obtaining a historical error log of at least one cloud application includes: Obtaining an error monitoring dimension determined by the user end, wherein the error monitoring dimension includes an application dimension and an error code dimension; When the error monitoring dimension is the application dimension, determining the target cloud application selected for monitoring by the user end, and obtaining all error logs of the target cloud application; When the application monitoring dimension is the error code dimension, the monitoring error code selected by the user end is determined, and the error logs whose error codes are the monitoring error codes in all error logs are obtained.

[0008] In one embodiment, after the step of obtaining a historical error log of at least one cloud application and extracting an error code from each of the error logs, the step further includes: Determine the error time, error level, error stack information and link information of the error from each of the error logs; Determine error code information according to the error log and the corresponding occurrence time, error level, stack information and link information, wherein the error code information at least includes the occurrence frequency, type, quantity and occurrence date of each error code; The error code information is sent to the user end.

[0009] In one embodiment, the step of sending the error code information to the user terminal includes: Generate an email based on the error code information, wherein the email contains the error code information and a corresponding background log jump link and link information jump link; The email is sent to the user terminal at a regular interval so that the user terminal can upload the manual analysis result corresponding to the unanalyzed error code.

[0010] In one embodiment, the error code includes analyzed error codes and unanalyzed error codes, and the step of obtaining historical error analysis results of each of the error codes includes: If the error code is an analyzed error code, an error analysis result corresponding to the analyzed error code is obtained from a preset error code analysis library, and the error analysis result is used as a historical error analysis result; If the error code is an unanalyzed error code, a manual analysis result corresponding to the unanalyzed error code uploaded by the user terminal is received, and the manual analysis result is stored in a preset error code analysis library as an error analysis result corresponding to the error code.

[0011] In one embodiment, after the step of analyzing the error log corresponding to the unanalyzed error code based on the target error code analysis model and determining the model analysis result, the step further includes: receiving an access request to a preset error code analysis library, and determining that the error code carried in the access request is a first error code; Searching the preset error code analysis library for an error analysis result corresponding to the first error code; The error analysis result is output.

[0012] In addition, to achieve the above purpose, the present application also proposes a cloud application error analysis device, the cloud application error analysis device comprising: An extraction module, used to obtain a historical error log of at least one cloud application, and extract an error code from each of the error logs; An acquisition module, used to acquire historical error analysis results of each of the error codes; A training module, used to use each of the error codes and the corresponding historical error analysis results as training data to perform model training on a preset error code analysis model to obtain a trained target error code analysis model, wherein the error code analysis model is used to predict the error analysis results of unanalyzed error codes, where the unanalyzed error codes are error codes for which there are no historical error analysis results; The analysis module is used to detect the error log of at least one cloud application in real time. If it is detected that there is an unanalyzed error code in the error log, the error log corresponding to the unanalyzed error code is analyzed based on the target error code analysis model to determine the model analysis result.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a cloud application error analysis device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the cloud application error analysis method described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the cloud application error analysis method described above are implemented.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the cloud application error analysis method described above.

[0016] The present application obtains the historical error log of at least one cloud application, extracts the error code from each error log; obtains the historical error analysis results of each error code, so as to quickly locate the error recorded in the error log; uses each error code and the corresponding historical error analysis results as training data to train the preset error code analysis model, and obtains a trained target error code analysis model, wherein the error code analysis model is used to predict the error analysis results of unanalyzed error codes, and the unanalyzed error codes are error codes for which there are no historical error analysis results. By building a model for automatically predicting unknown errors, the dependence on manual work is reduced; the error log of at least one cloud application is detected in real time, and if it is detected that there are unanalyzed error codes in the error log, the error log corresponding to the unanalyzed error code is analyzed based on the target error code analysis model to determine the model analysis results, and the unanalyzed error codes are analyzed in real time during daily monitoring, and a preliminary analysis result is predicted, which significantly shortens the error analysis time. In the present application, the human burden is significantly reduced by means of automation and intelligence, and the efficiency and accuracy of error handling are improved by the automated model. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 A flowchart of the first embodiment of the cloud application error analysis method of the present application is provided; Figure 2 A flowchart of the second embodiment of the cloud application error analysis method of the present application is provided; Figure 3 This is a brief flowchart of the cloud application error analysis method for this application; Figure 4 This is a schematic diagram of the module structure of the cloud application error analysis device according to an embodiment of the present application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the cloud application error analysis method in the embodiment of the present application.

[0020] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0022] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0023] This application embodiment provides a cloud application error analysis method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the cloud application error analysis method of the present application.

[0024] In this embodiment, the cloud application error analysis method includes steps S10 to S40: Step S10, obtaining a historical error log of at least one cloud application, and extracting an error code from each error log; It should be noted that during the operation of cloud applications, log files containing operation status and exception information will be generated. These logs record detailed information during the execution of the application, including but not limited to error information, warnings, debugging information, etc. Among them, the log containing error information is the error log, which directly reflects the error problems that occur during the operation of the application. In the error log, the error code corresponding to the error is usually carried. The error code is a code that identifies a specific error type. Different error codes correspond to different error types or causes of the problem. The error code can be used to quickly locate the source of the error. The historical error log is the error log stored within a preset time period.

[0025] In addition, it should be noted that for log acquisition, you can configure the log collection service of the cloud platform to automatically capture log information from each application instance regularly or in real time. You can use log collection tools (such as Logstash, Fluentd, etc.) or log services provided by cloud service providers (such as AWS CloudWatch Logs, AzureMonitor, etc.) to achieve unified management and acquisition of cloud application logs. You can also use the API interface provided by some cloud applications to request application error logs programmatically by developers or operation and maintenance personnel. Or you can directly export log files from the application server manually or by script. For error code extraction, you can use regular expressions to match error codes in a specific format from the log text; for applications that follow the structured log format, you can directly parse specific fields in the log to extract error codes; or use specialized log analysis software (such as ELK Stack, Splunk, etc.) to automatically identify and extract error codes in the log through the built-in error code extraction function in the software tool.

[0026] It is understandable that by collecting error information from various cloud applications on the cloud platform and extracting key error codes, basic data is provided for subsequent error analysis, problem location, and model training.

[0027] In a feasible implementation, step S10 may include steps A10 to A30: Step A10, obtaining the error monitoring dimension determined by the user end, wherein the error monitoring dimension includes an application dimension and an error code dimension; It should be noted that the application dimension allows users to select specific applications as monitoring objects based on multiple applications deployed in the cloud platform, which can focus resources and attention on key applications, quickly locate and solve problems that affect core business, reduce attention to non-critical areas, and improve operation and maintenance efficiency. In the case of the error code dimension, users can select specific error codes as monitoring focuses, which is suitable for situations where problems caused by known error codes occur frequently, or specific error codes represent serious problems. By directly tracking specific error codes, you can deeply analyze the reasons behind them, optimize and prevent them in a targeted manner, avoid similar errors from recurring, and improve the stability and reliability of the system.

[0028] It should be noted that by providing flexible monitoring dimension options to improve the efficiency and pertinence of error analysis, it is possible to avoid the problem of directly monitoring and analyzing all error information, reduce resource consumption, and improve problem location efficiency.

[0029] Step A20, when the error monitoring dimension is the application dimension, determine the target cloud application selected for monitoring by the user end, and obtain all error logs of the target cloud application; It should be noted that when the user determines the monitoring dimension as the "application" dimension, it expresses a high degree of concern about the status of a specific cloud application. This may be because the application carries critical business processes, or there have been frequent abnormal situations recently, and close monitoring is required to prevent potential service interruptions.

[0030] Step A30, when the application monitoring dimension is the error code dimension, determine the monitoring error code selected by the user end, and obtain the error logs whose error codes are the monitoring error codes in all error logs.

[0031] It should be noted that the monitoring error code is one or a group of error codes that users select based on historical experience, system log analysis, or the urgency of the current problem. These error codes are usually associated with frequently occurring problems or potential serious vulnerabilities in the system. The system adjusts the monitoring strategy and log filtering rules based on the error code selected by the user, and filters out log records that match the monitoring error code from all error logs.

[0032] It is understandable that the user side chooses to monitor specific error codes, indicating that there are clear problem directions or known failure modes, and hopes to quickly find the root cause of the problem and optimize system stability by centrally analyzing the error logs of these error codes. This can reduce troubleshooting time, improve operation and maintenance efficiency, and avoid business interruptions or reduced user experience caused by specific errors.

[0033] In this implementation, the error monitoring dimension determined by the user end is obtained, and the user can choose to monitor a specific application or a specific error code according to the business priority or the recent problem frequency point; when the error monitoring dimension is the application dimension, the target cloud application selected by the user end for monitoring is determined, and all error logs of the target cloud application are obtained. The error logs of the specific application are collected centrally, which is conducive to quickly identifying problems in the application and accelerating problem location and resolution; when the application monitoring dimension is the error code dimension, the monitoring error code selected by the user end is determined, and the error logs with the error code of the monitoring error code in all error logs are obtained, allowing the user to flexibly configure the monitoring strategy and focus on a specific error code, which can help the operation and maintenance team to deeply analyze the reasons behind the error code, optimize and repair it in a targeted manner, and avoid the recurrence of similar errors. Dynamically adjusting the monitoring strategy according to user needs can achieve more efficient and targeted cloud application error analysis.

[0034] Step S20, obtaining historical error analysis results of each error code; It should be noted that the preset error code analysis library is an error code asset library that stores historical error analysis results. Each historical error analysis result may include error code, error description, historical cases, error frequency and trend, etc. Among them, the error description is used to explain in detail the specific error meaning represented by the error code, the historical case records the specific context, trigger conditions, impact range, etc. of the error code in the past, and the error frequency and trend statistics the frequency of error code occurrence and the change trend over time.

[0035] It can be understood that for the extracted error code, the corresponding error history analysis record is searched from the pre-established error code analysis library to quickly obtain information such as the cause of the error and the scope of impact, so that by drawing on historical experience, the problem can be located more quickly. At the same time, the analysis results corresponding to the error code can be quickly obtained to improve data acquisition efficiency.

[0036] Step S30, using each error code and the corresponding historical error analysis result as training data to perform model training on a preset error code analysis model to obtain a trained target error code analysis model, wherein the error code analysis model is used to predict the error analysis result of an unanalyzed error code, where the unanalyzed error code is an error code for which there is no historical error analysis result; It should be noted that useful features can be extracted from the error code and its historical analysis results, such as the error code itself, the cause of the error, the time and location of the error, the error frequency, related log entries, etc. These features can be used as training data and become the basis for model learning. The error code analysis model obtained after training can use the trained model to predict error codes that are not included in the preset error code analysis library, and can obtain possible error types, causes and other information.

[0037] In the subsequent process, the newly encountered error codes and their analysis results can be continuously fed back to the model, and the error code analysis library and model can be continuously updated and optimized to achieve iterative updates of the model.

[0038] It is understandable that by establishing an analysis model that can predict unseen error codes, even when there is no corresponding historical record in the preset error code analysis library, new errors can be effectively predicted and classified based on the analysis results of known error codes, thereby improving the ability to predict and handle unknown errors, thereby improving the automation and intelligence level of cloud application error analysis.

[0039] Step S40, detecting the error log of at least one cloud application in real time, if it is detected that there is an unanalyzed error code in the error log, analyzing the error log corresponding to the unanalyzed error code based on the target error code analysis model to determine the model analysis result.

[0040] It should be noted that through the preset error code analysis library, it is possible to determine whether the error code is an analyzed error code or an unanalyzed error code. Once an unanalyzed error code is detected, the prediction process of the error code analysis model is immediately triggered. It is necessary to quickly call the trained machine learning model and input the error log or context information corresponding to the unanalyzed error code. The model extracts relevant features from it, and based on the relevant feature mapping results, uses the relationship between the learned error code and the error analysis result to perform predictive analysis and output an error analysis result, including but not limited to the error type, possible causes, and scope of impact.

[0041] It is understandable that by real-time detection of unanalyzed error codes appearing in the error log and using the previously trained error code analysis model, these new errors can be quickly and preliminarily analyzed and predicted, thus reducing the time for manual troubleshooting while maintaining a high prediction accuracy and improving the overall efficiency of error analysis.

[0042] In a feasible implementation, after step S40, the cloud application error analysis method may further include steps S50 to S70: Step S50, receiving an access request to a preset error code analysis library, and determining that the error code carried in the access request is a first error code; It should be noted that the system needs to provide a stable API interface or web service endpoint that supports receiving requests for users to query the analysis results of specific error codes. This interface can be provided by a question-and-answer robot. The internal logic of the system needs to be able to identify and confirm the error code mentioned in the request and mark it as the first error code. By receiving and parsing requests, it provides users with automated error code query services.

[0043] Step S60, searching a preset error code analysis library for an error analysis result corresponding to the first error code; Step S70, outputting error analysis results.

[0044] It should be noted that the error analysis results usually include error description, error cause, impact scope, solution, preventive measures, related documents or links, etc.

[0045] The error analysis result corresponding to the first error code is fed back to the requesting party in an appropriate form and channel. If the user queries the error code through the question-and-answer robot, the system feeds back to the user in the form of the robot's answer.

[0046] In this implementation, query requests from internal and external users are received, and these requests require analysis information of specific error codes. The first error code is determined from the query request, and an accurate search is performed in the error code analysis library based on the first error code to obtain the historical analysis record and solution of the error code, and the queried error analysis results are returned to the requesting party to provide the user with the information and guidance needed to solve the problem. By building a query service for error codes for external personnel, from receiving requests, searching and matching to outputting results, it can not only help external users quickly locate and solve known problems, but also improve user experience.

[0047] In this embodiment, by obtaining the historical error log of at least one cloud application, the error code is extracted from each error log; the historical error analysis results of each error code are obtained, so as to quickly locate the error recorded in the error log; each error code and the corresponding historical error analysis results are used as training data to train the preset error code analysis model to obtain a trained target error code analysis model, wherein the error code analysis model is used to predict the error analysis results of unanalyzed error codes, and unanalyzed error codes are error codes for which there are no historical error analysis results. By building a model for automatically predicting unknown errors, the dependence on manual work is reduced; the error log of at least one cloud application is detected in real time, and if it is detected that there is an unanalyzed error code in the error log, the error log corresponding to the unanalyzed error code is analyzed based on the target error code analysis model to determine the model analysis result, and the unanalyzed error code is analyzed in real time during the daily monitoring process to predict a preliminary analysis result, which significantly shortens the error analysis time. In this embodiment, the human burden is significantly reduced by means of automation and intelligence, and the efficiency and accuracy of error handling are improved by the automated model.

[0048] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 Before step S10, the cloud application error analysis method further includes steps S01 to S03: Step S01, determining the error time, error level, error stack information and link information of the error from each error log; It should be noted that the error time is the timestamp of the error log, which is usually read directly through the timestamp field in the log file. It can determine the specific time when the error occurred, and can be used to track the order of error occurrence, associate with other events in the same time period, evaluate the scope of fault impact, and formulate a recovery plan. Log records usually include error level labels, and the error level reflects the severity of the error. The stack information provides the function call sequence when the error occurs, including file name, function name, line number, etc. It is the key to locating the error location and analyzing the cause of the error. It usually exists in the log in the form of a string. It is necessary to extract the information of each layer of call through regular expressions, and it can also be interpreted in combination with code logic and operating environment. The link information records the request path when the error occurs, including the service call chain, API path, parameters, etc., which can be used to reproduce the error scenario and understand the interaction anomalies between systems. The link information may be embedded in the log text or recorded in a specific format, or displayed on a separate page.

[0049] It is understandable that accurately identifying and collecting key elements in the error log, including the time when the error occurred, the error level, the stack information and the link information, can help understand the nature of the error, the environment in which it occurred, the scope of impact and trace the source of the error, providing a direct basis for subsequent error classification, priority sorting, cause analysis, impact assessment and solution formulation.

[0050] Step S02, determining error code information according to the error log and the corresponding occurrence time, error level, stack information and link information, wherein the error code information at least includes the occurrence frequency, type, quantity and occurrence date of each error code; It should be noted that the error code occurrence frequency is the number of times each error code appears within a specific time range, which can identify the most common errors. According to the nature of the error corresponding to the error code, it is classified into different error types, such as system errors, logical errors, permission issues, network errors, etc. The number of errors records the total number of different error codes that appear, as well as the number of each type of error code. The occurrence date records the first and most recent date of each error code, analyzes whether the error is an occasional or persistent problem, and the error change trend over time, to provide a basis for predicting future error occurrences.

[0051] By thoroughly analyzing the error code information, it not only provides data support for the rapid resolution of current problems, but also lays a solid foundation for long-term system optimization and the formulation of preventive maintenance strategies.

[0052] Step S03, sending error code information to the user end.

[0053] It should be noted that there are many ways to send, such as interface push, which pushes the error code information to the user end in real time through the API interface. The user can be the monitoring platform used by the development team, the management panel of the operation and maintenance personnel, or even integrated into a third-party fault management tool. It can also be an email notification, which summarizes the error code information into a report and sends it to the relevant responsible person or team mailbox via email. It can also be integrated with instant messaging tools, which integrates the error code information into instant messaging platforms such as Slack and DingTalk through robots or plug-ins to achieve instant reminders and discussions.

[0054] By promptly feeding back the processed error code information to the user end, the fault response speed is improved, communication and collaboration are promoted, and the user experience is optimized.

[0055] In a feasible implementation manner, step S03 may include A10 to A20: Step A10, generating an email based on the error code information, wherein the email includes the error code information and the corresponding background log jump link and link information jump link; It should be noted that the background log records various states and events during system operation, including the specific time point when the error occurred, the value of related variables, stack traces, etc. By embedding a link directly pointing to the relevant error log entry in the email, the recipient can go directly to the error site with one click, without having to manually search in the huge log file, which greatly saves time. Link information jump links help understand the context in which the error occurred, especially when it involves multiple service interactions or complex system architectures. Link tracing technology can show the complete path of the request from the client to the server, including all service nodes passed, time consumption, response status, etc. Providing links to link information enables analysts to quickly grasp the full picture of the request and identify where the problem occurred.

[0056] Ensure that responsible personnel receive detailed notifications about errors in the system quickly and have direct access to key information sources that can help diagnose the problem. Sending this information via email is both formal and traceable, and adapts to the communication habits of most work environments.

[0057] Step A20, regularly sending an email to the user terminal so that the user terminal can upload the manual analysis result corresponding to the unanalyzed error code.

[0058] It should be noted that scheduled sending means that the system will automatically send email reminders to the user at preset time intervals. Setting scheduled sending helps to keep track of the progress of the problem and prevent the problem from being missed or forgotten. After receiving the email, the user conducts a detailed manual analysis based on the error code. The analysis may include code review, specific scenario reproduction, log in-depth mining, etc. Subsequently, the user uploads their analysis results through designated channels (such as replying to emails, filling out online forms, or using dedicated error reporting tools). The manual analysis results can include the root cause of the error, temporary solutions, recommended repair measures, etc.

[0059] It is understandable that in order to supplement the possible blind spots of automated analysis, more comprehensive and in-depth error causes and solutions can be obtained through direct feedback from users or in-depth analysis by human experts.

[0060] In this implementation, the system collects and organizes all necessary information related to the error code, usually including the error code itself, the time of occurrence, the scope of impact, a brief description, and the associated system modules or functions. For each error log, the system automatically generates a URL that directly links to the error log. In this way, the user can directly jump to the detailed log entry of the error after clicking the link, without manual search, which greatly improves the troubleshooting efficiency. In addition to the background log, the system will also generate a link trace (Trace ID) or a jump link for the relevant system call chain, so that the user can quickly view the context of the error and the entire request link, helping to understand the cause and effect of the error. Using the above-organized error code information, background log jump link, and link information jump link, the system automatically builds a detailed error report email. Set a scheduled task to send these emails to the specified user end (such as the development team, operation and maintenance team, or customer support team) at a pre-defined frequency (such as daily, weekly, or dynamically adjusted according to the urgency of the error code). Scheduled sending can ensure the timeliness of information and avoid omission or overload of information. The email should also include clear instructions on how to upload their manual analysis results for unanalyzed error codes. This not only achieves efficient delivery of error code information, but also promotes active participation and feedback from the user end.

[0061] In an embodiment of the present application, the error time, error level, stack information and link information of the error are determined from each error log to provide basic data for subsequent analysis. This information is the key to understanding the nature of the error, the occurrence scenario and tracing the source of the error; the error code information is determined according to the error log and the corresponding occurrence time, error level, stack information and link information, wherein the error code information at least includes the frequency, type, quantity and occurrence date of each error code; the error code information is sent to the user end, and the user can grasp the error status of the system without directly viewing the original log, and quickly decide whether to adjust the monitoring strategy or take repair actions. In this embodiment, from the analysis of the original log to the information extraction, and then to the instant feedback of the information, the efficiency and quality of error handling can be improved, which not only provides the necessary data support for the subsequent specific error code monitoring, but also ensures the initiative of error management, that is, before the impact of the error expands, it can be systematically identified and prioritized, thereby optimizing resource allocation and reducing fault recovery time.

[0062] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated later. On this basis, the error code includes an analyzed error code and an unanalyzed error code, and step S20 may include steps S21 to S22: Step S21, if the error code is an analyzed error code, obtaining a historical error analysis result corresponding to the analyzed error code from a preset error code analysis library; It is understandable that when it is detected that the error code belongs to an analyzed error code, the existing knowledge base resources are quickly used to obtain the historical error analysis results of the error code to avoid duplication of work, so that the operation and maintenance team can take action more quickly and reduce system interruption time.

[0063] Step S22: If the error code is an unanalyzed error code, a manual analysis result corresponding to the unanalyzed error code uploaded by the user is received.

[0064] It should be noted that the user end can upload the manual analysis results of the unanalyzed error code based on the email sent in step A20. After receiving the email, the user end uploads the detailed analysis report, solution attempt, error scenario description and other information of the unanalyzed error code through the designated channel according to the actual analysis situation. After the system receives this information, it integrates the analysis results submitted by the user into the preset error code analysis library, creates a new record for the error code or updates the existing temporary record. It can not only help solve current unknown errors, but also enhance the system's self-learning and adaptability by continuously accumulating new error analysis results, and improve the efficiency of handling similar problems in the future.

[0065] In this embodiment, the system first determines whether the current error code exists in the preset error code analysis library. This analysis library is a continuously updated database that contains historical error codes and their corresponding analysis results, solutions or suggestions. If the error code matches successfully, the system directly retrieves the historical analysis results of the error code from the library. When the error code is not found in the preset error code analysis library, the system activates the receiving mechanism, waits for and accepts the manual analysis results uploaded by the user. The manual analysis results are determined by the user based on actual experience, testing or in-depth research, and may include the environment in which the error occurred, specific manifestations, possible causes and suggested solutions. This embodiment realizes the intelligent classification and processing of error codes, which not only utilizes existing resources to accelerate the resolution of known problems, but also expands the coverage of solutions through the user participation mechanism, improves the system's response capabilities to unknown or new problems, improves efficiency and accuracy, and ensures that both existing solutions and newly discovered problems can be properly handled.

[0066] For example, to help understand the implementation process of the cloud application error analysis method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 3 , Figure 3 A brief flowchart of a cloud application error analysis method is provided. Specifically, numbers 0 to 9 are the execution order.

[0067] 1. Users subscribe to ZA24 application instances and PAAS application instances; 2. Error code collection: The monitoring tool pulls error details from the cloud platform every 15 minutes and stores information such as log occurrence time, level, stack, and link; 3. Error code statistics: Statistics on the frequency of occurrence of error codes in the log (the number of occurrences in the previous 1, 3, 7 and 30 days), type, quantity and date; 4. Email notification: Push the subscribed error code information via email at the time set by the user; 5. Positioning: Users can jump to the background log details and Beidou link details page through email to analyze and locate the problem; 6. Error code analysis: Users can view historical analysis reasons and assets for the same error code, and support submission of analysis results for unanalyzed error codes; 7. Archiving: Store manual and large model analysis results in the asset library for easy reference when they appear again later; 8. Training: Use the analyzed assets as source data for vector database and large model training; 9. AI analysis: After the large model learning is completed, preliminary analysis results are given for the newly appeared error codes; 10. Query: The vector database and large model training give results, which are put into the asset library after manual confirmation. Users can use the question-answering robot to quickly access the error code asset library and quickly obtain the root cause of the error code.

[0068] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the cloud application error analysis method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0069] This application also provides a cloud application error analysis device, please refer to Figure 4 , the cloud application error analysis device comprises: An extraction module 10 is used to obtain a historical error log of at least one cloud application and extract an error code from each of the error logs; An acquisition module 20 is used to acquire historical error analysis results of each of the error codes; A training module 30 is used to perform model training on a preset error code analysis model using each of the error codes and the corresponding historical error analysis results as training data to obtain a trained target error code analysis model, wherein the error code analysis model is used to predict the error analysis results of unanalyzed error codes, where the unanalyzed error codes are error codes for which there are no historical error analysis results; The analysis module 40 is used to detect the error log of at least one cloud application in real time. If it is detected that there is an unanalyzed error code in the error log, the error log corresponding to the unanalyzed error code is analyzed based on the target error code analysis model to determine the model analysis result.

[0070] The cloud application error analysis device provided by the present application adopts the cloud application error analysis method in the above embodiment, which can solve the technical problem that manual error analysis of cloud applications easily leads to low error processing efficiency and accuracy. Compared with the prior art, the beneficial effects of the cloud application error analysis device provided by the present application are the same as the beneficial effects of the cloud application error analysis method provided by the above embodiment, and other technical features in the cloud application error analysis device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0071] The present application provides a cloud application error analysis device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the cloud application error analysis method in the above-mentioned embodiment one.

[0072] Reference below Figure 5 , which shows a schematic diagram of the structure of a cloud application error analysis device suitable for implementing the embodiment of the present application. The cloud application error analysis device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The cloud application error analysis device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0073] like Figure 5As shown, the cloud application error analysis device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the xxx device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the cloud application error analysis device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a cloud application error analysis device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0074] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0075] The cloud application error analysis device provided by the present application adopts the cloud application error analysis method in the above embodiment, which can solve the technical problem that manual error analysis of cloud applications easily leads to low error processing efficiency and accuracy. Compared with the prior art, the beneficial effects of the cloud application error analysis device provided by the present application are the same as the beneficial effects of the cloud application error analysis method provided by the above embodiment, and other technical features in the cloud application error analysis device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0076] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0077] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0078] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the cloud application error analysis method in the above-mentioned embodiment.

[0079] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0080] The computer-readable storage medium may be included in the cloud application error analysis device; or may exist independently without being assembled into the cloud application error analysis device.

[0081] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the cloud application error analysis device, the cloud application error analysis device: obtains the historical error log of at least one cloud application, and extracts the error code from each of the error logs; obtains the historical error analysis results of each of the error codes; uses each of the error codes and the corresponding historical error analysis results as training data to perform model training on a preset error code analysis model to obtain a trained target error code analysis model, wherein the error code analysis model is used to predict the error analysis results of unanalyzed error codes, and the unanalyzed error codes are error codes for which there are no historical error analysis results; detects the error log of at least one cloud application in real time, and if it is detected that there are unanalyzed error codes in the error log, analyzes the error log corresponding to the unanalyzed error code based on the target error code analysis model to determine the model analysis results.

[0082] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0083] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0084] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0085] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned cloud application error analysis method, and can solve the technical problem that manual error analysis of cloud applications easily leads to low error handling efficiency and accuracy. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the cloud application error analysis method provided in the above-mentioned embodiment, and will not be repeated here.

[0086] The present application also provides a computer program product, including a computer program, which implements the steps of the cloud application error analysis method as described above when executed by a processor.

[0087] The computer program product provided by the present application can solve the technical problem that manual error analysis of cloud applications easily leads to low error handling efficiency and accuracy. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the cloud application error analysis method provided in the above embodiment, which will not be repeated here.

[0088] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A cloud application error analysis method, characterized in that: The cloud application error analysis method comprises: Obtaining historical error logs of at least one cloud application, and extracting error codes from each of the error logs; Obtaining historical error analysis results for each of the error codes; Using each of the error codes and the corresponding historical error analysis results as training data to perform model training on a preset error code analysis model, to obtain a trained target error code analysis model, wherein the error code analysis model is used to predict the error analysis results of unanalyzed error codes, where the unanalyzed error codes are error codes for which there are no historical error analysis results; The error log of at least one cloud application is detected in real time. If an unanalyzed error code is detected in the error log, the error log corresponding to the unanalyzed error code is analyzed based on the target error code analysis model to determine a model analysis result.

2. The cloud application error analysis method according to claim 1, characterized in that: The step of obtaining a historical error log of at least one cloud application includes: Obtaining an error monitoring dimension determined by the user end, wherein the error monitoring dimension includes an application dimension and an error code dimension; When the error monitoring dimension is the application dimension, determining the target cloud application selected for monitoring by the user end, and obtaining all error logs of the target cloud application; When the application monitoring dimension is the error code dimension, the monitoring error code selected by the user end is determined, and the error logs whose error codes are the monitoring error codes in all error logs are obtained.

3. The cloud application error analysis method according to claim 1, characterized in that: After the step of obtaining the historical error log of at least one cloud application and extracting the error code from each error log, the method further includes: Determine the error time, error level, error stack information and link information of the error from each of the error logs; Determine error code information according to the error log and the corresponding occurrence time, error level, stack information and link information, wherein the error code information at least includes the occurrence frequency, type, quantity and occurrence date of each error code; The error code information is sent to the user end.

4. The cloud application error analysis method according to claim 3, characterized in that: The step of sending the error code information to the user terminal includes: Generate an email based on the error code information, wherein the email contains the error code information and the corresponding background log jump link and link information jump link; The email is sent to the user terminal at a regular interval so that the user terminal can upload the manual analysis result corresponding to the unanalyzed error code.

5. The cloud application error analysis method according to claim 4, characterized in that: The error codes include analyzed error codes and unanalyzed error codes, and the step of obtaining historical error analysis results of each of the error codes includes: If the error code is an analyzed error code, an error analysis result corresponding to the analyzed error code is obtained from a preset error code analysis library, and the error analysis result is used as a historical error analysis result; If the error code is an unanalyzed error code, a manual analysis result corresponding to the unanalyzed error code uploaded by the user terminal is received, and the manual analysis result is stored in a preset error code analysis library as an error analysis result corresponding to the error code.

6. The cloud application error analysis method according to claim 1, characterized in that: After the step of analyzing the error log corresponding to the unanalyzed error code based on the target error code analysis model to determine the model analysis result, the method further includes: Receiving an access request to a preset error code analysis library, and determining that the error code carried in the access request is a first error code; Searching the preset error code analysis library for an error analysis result corresponding to the first error code; The error analysis result is output.

7. A cloud application error analysis device, characterized in that: The cloud application error analysis device comprises: An extraction module, used to obtain a historical error log of at least one cloud application, and extract an error code from each of the error logs; An acquisition module, used to acquire historical error analysis results of each of the error codes; A training module, used to use each of the error codes and the corresponding historical error analysis results as training data to perform model training on a preset error code analysis model to obtain a trained target error code analysis model, wherein the error code analysis model is used to predict the error analysis results of unanalyzed error codes, where the unanalyzed error codes are error codes for which there are no historical error analysis results; The analysis module is used to detect the error log of at least one cloud application in real time. If it is detected that there is an unanalyzed error code in the error log, the error log corresponding to the unanalyzed error code is analyzed based on the target error code analysis model to determine the model analysis result.

8. A cloud application error analysis device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the cloud application error analysis method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the cloud application error analysis method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the cloud application error analysis method according to any one of claims 1 to 6 are implemented.

Images